Control your X/Twitter feed using a small on-device LLM
We built a Chrome extension and iOS app that filters Twitter's feed using Qwen3.5-4B for contextual matching. You describe what you don't want in plain language—it removes posts that match semantically, not by keyword. What surprised us was that because Twitter's ranking algorithm adapts based on what you engage with, consistent filtering starts reshaping the recommendations over time. You're implicitly signaling preferences to the algorithm. For some of us it "healed" our feed. Currently running inference from our own servers with an experimental on-device option, and we're working on fully on-device execution to remove that dependency. Latency is acceptable on most hardware but not great on older machines. No data collection; everything except the model call runs locally. It doesn't work perfectly (figurative language trips it up) but it's meaningfully better than muting keywords and we use it ourselves every day. Also promising how local / open models can now start giving us more control over the algorithmic agents in our lives, because capability density is improving.
41 years sea surface temperature anomalies
Show HN: 41 years sea surface temperature anomalies
I built a local data lake for AI powered data engineering and analytics
I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively. You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You don’t have to setup local LLMs, it comes built in. This is completely free. No cloud account required. Downloading the software - https://getnile.ai/downloads Watch a demo - https://www.youtube.com/watch?v=C6qSFLylryk Check the code repo - https://github.com/NileData/local This is still early and I'd genuinely love your feedback on what's broken, what's missing, and if you find this useful for your data and analytics work.
Tired of logic in useEffect, I built a class-based React state manager
Show HN: Tired of logic in useEffect, I built a class-based React state manager
Orange Juice
Show HN: Orange Juice – Small UX improvements that make HN easier to read
Is Hormuz open yet?
I built this because I was interested in the data. Didn't fully get it to what I wanted, but thought I'd share it nonetheless. Maybe someone has better data sources they could share! Turns out live ship tracking APIs are expensive so I manually just copied the json from https://www.marinetraffic.com/en/ais/home/centerx:57.4/cente... I'll probably have an ai agent do the same thing on some cron interval, if this gets any fanfare. To actually know if the port is open without live ship tracking I found https://portwatch.imf.org/pages/cb5856222a5b4105adc6ee7e880a... which was perfect, except it has 4 day lag! I also thought of adding news feed parsing or prediction market data to get a more definitive answer on if it's open right when you load it, but I spent a few hours and am gonna move on for now.
Eff
Digital Hopes, Real Power: How the Arab Spring Fueled a Global Surveillance Boom
An interactive map of Tolkien's Middle-earth
An interactive map of Tolkien’s Middle-earth, with events from across the legendarium plotted as markers. I have been commuting a fair bit between the East and West coast, and thanks to American Airlines' free onboard WiFi, I was able to vibe-code a full interactive map of Middle-earth right from my economy seat at the back of the bus. It's rather amazing how much an LLM knows about Tolkien's work, and it was fun to delve into many of the nooks and crannies of Tolkien's lore. Some features: - Plot on the map the journey of the main characters in both The Hobbit and The Lord of the Rings. - Follow a list of events in the chronological Timeline - Zoom in on the high-def map and explore many of the off-the-main-plotline places - Use the 'measure distances' feature to see how far apart things are. I also had a lot of fun learning about tiling to allow for efficient zooming. If you are anything like me, this should provide a fun companion to reading the books or watching the movies (note that on this site, I followed the book narrative, and did not include Peter Jackson's many departures) If you get the chance to check it out, I would love more feedback, and if there is demand, I might do the same for Game of Thrones.
Output.ai
Show HN: Output.ai - OSS framework we extracted from 500+ production AI agents
A cartographer's attempt to realistically map Tolkien's world
Show HN: A cartographer's attempt to realistically map Tolkien's world
Per-user isolated environments for AI agents
Show HN: Per-user isolated environments for AI agents
Freestyle
We’re Ben and Jacob, cofounders of Freestyle (https://freestyle.sh). We’re building a cloud for Coding Agents. For the first generation of agents it looked like workflows with minimal tools. 2 years ago we published a package to let AI work in SQL, at that time GPT-4 could write simple scripts. Soon after the first AI App Builders started using AI to make whole websites; we supported that with a serverless deploy system. But the current generation is going much further, instead of minimal tools and basic serverless apps AI can utilize the full power of a computer (“sandbox”). We’re building sandboxes that are interchangeable with EC2s from your agents perspective, with bonus features: 1. We’ve figured out how to fork a sandbox horizontally without more than a 400ms pause in it. That's not forking the filesystem, we mean forking the whole memory of it. If you’re half way down a browser page with animations running, they’ll be in the same place in all the forks. If you’re running a minecraft server every block and player will be in the same place on the forks. If you’re running a local environment and an error comes up in process that error will be there in all the forks. This works for snapshotting as well, you can save your place and come back weeks later. 2. Our sandboxes start in ~500ms. Demo: https://www.loom.com/share/8b3d294d515442f296aecde1f42f5524 Compared with other sandboxes, our goal is to be the most powerful. We support full Linux + hardware-virtualization, eBPF, Fuse, etc. We run full Debian with multiple users and we use a systemd init instead of runc. Whatever your AI expects to work on debian should work on these vms, and if it doesn’t send a bug report. In order to make this possible, we’ve moved to our own bare metal racks. Early in our testing we realized that moving VMs across cloud nodes would not have acceptable performance properties. We asked Google Cloud and AWS for a quote on their bare metal nodes and found that the monthly cost was equivalent to the total cost of the hardware so we did that. Our goal is to build the necessary infrastructure to replicate the human devloop on the massively multi-tenant scale of AI, so these VMs should be as powerful as the ones you’re used to, while also being available to provision in seconds.